The world wide web (WWW) presents many commercial opportunities by presenting information to users to purchase goods and/or services. Tracking user behaviors or trends allows for such opportunities, and predicting a user's future actions can provide greater opportunities to relevant information.
Behavioral targeting uses information collected based on an individual user's online behavior. Such information can include web pages/websites the user has visited, or search queries the user has performed. In particular, such web pages/websites are selected to provide services and content to the individual user. It is desirable to build user behavior models that understand and differentiate between users.
There can be many benefits and uses of data gathered from a user behavior model. For example, if an advertiser understands which user will likely purchase its product, the advertiser can design a more focused advertisement campaign to target relevant users. As another example, if a content publisher knows what a user is going to be interested in the near future, the content publisher can recommend the appropriate web pages to satisfy the user's information need. As yet another example, if a search engine captures the user's online intent in advance, the search engine can not only address the user's search need, but can also facilitate and simplify the user's activities related to their current needs.
Traditional approaches may investigate on short-term, immediate or aggregated user behaviors, where user behaviors occurring at different times are aggregated together. Therefore, temporal information is lacking as to user behavior. If a user's future action can be predicted and identified in time or in advance, not only can the user's current need be satisfied, but the user's future online activities can be facilitated and simplified.